Kursanalys: Statistical Methods in Applied Computer Science, statmet10

For the 2009 analysis, see 'andra omgångar'

Statistical Methods in Applied Computer Science, 2D1447 6hp, (6 ECTS).

Course given in Spring 2010

Instructor: Stefan Arnborg

Lectures: 18 hours
Recitals: 12 hours
Individual Advising: ca 15 hours

Registered students: 12 Master's students(DD2447)+3 doctoral student(FDD3342)

Course Literature:

Lecture Notes: Statistical Methods in Applied Computer Science
Ed Jaynes Lecture notes, Ch 1-3 and 5
The material on conformal prediction was clarified in the lecture notes, and some articles on variational Bayes were added. The latter is planned to go into the lecture notes for the 2011 class.

EXAMINATION

Course has one examined moment, labelled as Homework
9 of 12 Master's students passed to grade C, two are currently doing Master's tests and three (of the 12) are completing late homeworks.
Most students where non-Swedish speaking, so teaching was in English, Advising in Swedish or English.

Throughput: 75% (9 Master's students had passed to grade C on June 14 2010, by doing homeworks). The PhD students are still working on their projects, but two of last years PhD students completed their projects in 2010.

Course Goals:

This course summarizes statistical and probabilistic methods used in applied Computer Science - statistical aspects of Data Mining, Knowledge Discovery, Machine learning and Information Fusion with a Bayesian outlook.

LEARNING GOALS

After successfully taking this course, you will be able to:

-motivate the use of uncertainty management and statistical methodology in computer science applications, as well as the main methods in use,

-account for algorithms used in the area and use the standard tools,

-critically evaluate the applicability of these methods in new contexts, and design new applications of uncertainty management,

-follow research and development in the area.

Changes made in 2009:

No change, but some extra rehearsal was introduced. Lectures and material on Gammerman/Vovk inference and recommender systems was popular.

SUMMARY

I liked giving the course. I think I will work harder next time to get more active participation during lectures.

EXAMINATION

OK. individual examination is somewhat time-consuming, but with a small class it probably breaks even.

LITERATURE:

Seemed to work fine. I will do some refinements in the compendium, but just now I have not found new stuff to throw in. Maybe oracle property in feature selection, a hot but difficult topic in high-throughput bioinformatics.

EXAMINATION

Här kommer kursanalysen att finnas efter kursens slut.